diversity

Article

Assessing Assemblage Composition of Reproductively MatureResource Fishes at a Community Based Subsistence FishingArea (CBSFA)

Rebecca M. Weible 1,2,*, Ku‘ulei S. Rodgers 1,2 , Alan M. Friedlander 2,3 and Cynthia L. Hunter 1

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Citation: Weible, R.M.; Rodgers, K.S.;

Friedlander, A.M.; Hunter, C.L.

Assessing Assemblage Composition

of Reproductively Mature Resource

Fishes at a Community Based

Subsistence Fishing Area (CBSFA).

Diversity 2021, 13, 114.

https://doi.org/10.3390/d13030114

Academic Editor: Bert W. Hoeksema

Received: 31 January 2021

Accepted: 26 February 2021

Published: 7 March 2021

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4.0/).

1 School of Life Sciences, University of Hawai‘i at Manoa, Honolulu, HI 96822, USA;kuuleir@hawaii.edu (K.S.R.); cindyh@hawaii.edu (C.L.H.)

2 Hawai‘i Institute of Marine Biology, University of Hawai‘i at Manoa, Kane‘ohe, HI 96744, USA;friedlan@hawaii.edu

3 National Geographic Pristine Seas, Washington, DC 20036, USA* Correspondence: rweible@hawaii.edu

Abstract: Nearshore fisheries in Hawai‘i have been steadily decreasing for over a century. Marineprotected areas (MPAs) have been utilized as a method to both conserve biodiversity and enhancefisheries. The composition of resource fishes within and directly outside of the recently establishedHa‘ena Community Based Subsistence Fishing Area (CBSFA) on the island of Kaua‘i were assessedto determine temporal and spatial patterns in assemblage structure. In situ visual surveys of fishes,invertebrates, and benthos were conducted using a stratified random sampling design to evaluatethe efficacy of the MPA between 2016 and 2020. L50 values—defined as the size at which half of theindividuals in a population have reached reproductive maturity—were used as proxies for identifyingreproductively mature resource fishes both inside and outside the CBSFA. Surveys between 2016 and2020 did not indicate strong temporal or spatial changes in overall resource fish assemblage structure;however, some species-specific changes were evident. Although overall resource species diversityand richness were significantly higher by 2020 inside the MPA boundaries, there is currently nostrong evidence for a reserve effect.

Keywords: resource fishes; assemblage composition; reproductive maturity; community basedsubsistence fishing area; marine protected area; L50

1. Introduction

Marine protected areas (MPAs) have been increasingly employed as an effectivemethod for managing overfished nearshore coral reef ecosystems [1,2]. MPAs have beenshown to increase fish biomass, diversity, and reproductive output within the protectedarea, as well as enhance adjacent areas via adult and larval spillover [1,3]. However, MPAsare not a panacea for overfished stocks, poor habitat quality, or ineffective management orenforcement [1,3–6]. The effectiveness of a MPA is often reliant on careful considerationof key features, such as size, shape, configuration, larval connectivity and recruitment,life-history traits, habitat types, enforcement, and community support [3,5–7].

Nearshore fisheries declines in the State of Hawai‘i over the last century have pavedthe way for implementation of some level of protection in ~17% of state waters, yet only3.4% of nearshore waters are considered to be highly protected [2]. It is within these few,highly protected areas where resource fish biomass is substantially greater compared tolower or non-protected areas [8]. Additionally, with such a wide variety of MPA features,restrictions, and enforcement comes varying degrees of success. Contrasting examplesinclude the successful recovery of herbivorous species in the Kahekili Herbivore FisheriesManagement Area (KHFMA) on Maui [7] and the rapid depletion of reefs upon the openingof the Waikıkı-Diamond Head Shoreline Fisheries Management Area on O‘ahu whererestrictions to fishing are implemented on a yearly rotational basis [4]. In general, most of

Diversity 2021, 13, 114. https://doi.org/10.3390/d13030114 https://www.mdpi.com/journal/diversity

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the MPAs in Hawai‘i follow contemporary styles of management, with the exception of afew community-based subsistence fishing areas (CBSFAs).

Communities in Hawai‘i have increasingly explored the development of co-managementpartnerships between state resource management agencies and community groups toincorporate aspects of traditional ecological knowledge and customary marine tenureand to devolve some management authority to local scales where it was traditionallybased [9,10]. Despite efforts to re-establish local marine stewardship, CBSFAs are locatedin only three communities: Mo‘omomi on Moloka‘i, Miloli‘i on Hawai‘i, and Ha‘ena onKaua‘i. In 2015, the Ha‘ena CBSFA became the first of its kind to officially institute andenforce rules and regulations, which were drafted and finalized through collaborationfrom the Ha‘ena community. The following year, the Hawai‘i Department of Land andNatural Resources (DLNR), the Division of Aquatic Resources (DAR), and the Divisionof Boating and Ocean Recreation (DOBOR), partnered with the University of Hawai‘i,Hawai‘i Institute of Marine Biology (UHHIMB), Coral Reef Ecology Lab (CREL), and theCoral Reef Assessment and Monitoring Program (CRAMP) to conduct surveys at Ha‘enaCBSFA to determine the efficacy of the rule changes over time [11]. Baseline surveys wereconducted by the University of Hawai’i Fisheries Ecology Research Laboratory (UH FERL)between 2013 and 2014 on the nearshore shallow reef flats before the regulations wereapproved [12]. These baseline surveys were not spatially representative of the areas coveredin the 2016–2020 surveys and were therefore not included in this study.

This current study focuses on monitoring assemblage composition patterns of repro-ductively mature resource fish species throughout the full five-year monitoring period inan attempt to determine the efficacy of the CBSFA. Resource fish species were defined bythe Ha‘ena community and the reproductive maturity of these species was determinedusing L50 values derived from previous studies (Table A1) [13–27]. The objective of thisstudy was to examine fish assemblage structure of reproductively mature resource fishesthroughout this monitoring period, so as to examine the efficacy of the Ha‘ena CBSFA rulesenacted in 2015 and to provide information for adaptive management strategies relative tothe existing rules and regulations.

2. Materials and Methods

Ha‘ena is located on the north shore of Kaua‘i Island within the Hawaiian Archipelago.Coral reef structures extend along the inner reef of the Ha‘ena coast in shallow water, andalong the forereef into deeper depths (~20 m). Its location on the north shore exposesHa‘ena’s reefs to high wave energy and flushing year round, but particularly duringwinter months (November–March) when large North Pacific swells regularly generatewaves in excess of 10 m. Several streams enter the ocean both within and adjacent tothe CBSFA boundaries. These boundaries extend 1610 m (~1 mile) offshore and 5633 m(~3.5 miles) along the coastline [11,28] (Figure 1a). There are varying fishing restrictionswithin the CBSFA boundaries, including a designated area on the shallow backreef whereall fishing is prohibited. This area is referred to as the Makua Pu‘uhonua, which translatesto “nursery area” in Hawaiian, and was set aside to protect juvenile fishes during thiscritical life-history phase.

2.1. Sample Design

Surveys were conducted both within and directly east of the CBSFA (hereafter referredto as “inside” and “outside”) along the coast of Ha‘ena. Due to varying benthic structure,the Na Pali State Park on the west side of the CBSFA boundaries was intentionally notsurveyed. A stratified random sampling design was used to pre-determine survey stationsto allow for spatial representation by depth, habitat type, and location (inside and outside).Approximately 100 random points were generated and stratified by depth (shallow < 7 m,deep ≥ 7 m) using ArcGIS 10.6.1 (Figure 1b). Points were overlaid on National Oceano-graphic and Atmospheric Administration (NOAA) habitat base maps [28]. Surveys wereconducted at the original pre-determined stations. If hazardous conditions were encoun-

Diversity 2021, 13, 114 3 of 14

tered, depth estimates were in error, or <50% of the substrate was hard bottom, then diversswam the depth contour at a pre-determined compass heading (<100 m from the originalsite) until safe conditions, accurate depths, and >50% hard substrate were reached.

Diversity 2021, 13, x FOR PEER REVIEW 3 of 15

(a) (b)

Figure 1. Hāʻena site maps of (a) community-based subsistence fishing area (CBSFA) boundaries, the restricted Makua puʻuhonua, the vessel transit boundary, and the ʻopihi (limpet, Cellana spp.) management area with coordinates [28] and (b) survey stations from all five years within and outside the CBSFA boundaries.

2.1. Sample Design Surveys were conducted both within and directly east of the CBSFA (hereafter

referred to as “inside” and “outside”) along the coast of Hāʻena. Due to varying benthic structure, the Nā Pali State Park on the west side of the CBSFA boundaries was intentionally not surveyed. A stratified random sampling design was used to pre-determine survey stations to allow for spatial representation by depth, habitat type, and location (inside and outside). Approximately 100 random points were generated and stratified by depth (shallow < 7 m, deep ≥ 7 m) using ArcGIS 10.6.1 (Figure 1b). Points were overlaid on National Oceanographic and Atmospheric Administration (NOAA) habitat base maps [28]. Surveys were conducted at the original pre-determined stations. If hazardous conditions were encountered, depth estimates were in error, or <50% of the substrate was hard bottom, then divers swam the depth contour at a pre-determined compass heading (<100 m from the original site) until safe conditions, accurate depths, and >50% hard substrate were reached.

2.2. Fish and Benthic Surveys Fish counts and sizes were estimated inside and outside the CBSFA using the Kauaʻi

Assessments of Habitat Utilization (KAHU) rapid assessment technique over a five year period [28]. This method employed 25 × 5 m belt transects [29], designed by the UH FERL Fish Habitat Utilization Study (FHUS). The time frame of this study included surveys con-ducted in August 2016 (n = 55 inside, n = 43 outside), August 2017 (n = 59 inside, n = 49 outside), August 2018 (n = 71 inside, n = 32 outside), and June and August in 2019 (n = 58 inside, n = 40 outside), and June and August in 2020 (n = 79 inside, n = 44 outside). At each station, a 25 m transect was deployed in the direction of a pre-determined compass bearing (0°, 90°, 180°, or 270°). All fish species, counts, and sizes (total length [cm]) were recorded within a 5-m swath (125 m2 total area per transect) for a minimum of 10 min [28]. Fish surveyors annually participated in calibration dives to account for observer variability.

A benthic surveyor followed the fish diver to quantify habitat types associated with each transect [28]. Photographs were taken of the substrate on a previously calibrated Cannon S100 camera at every 1 m mark at a 90° angle to the transect (n = 26). Invertebrate (urchins and sea cucumbers) abundances were also recorded. The surveyor also enumer-ated macroinvertebrates within a 2 m × 25 m swath (50 m2) along the transect line. The

Figure 1. Ha‘ena site maps of (a) community-based subsistence fishing area (CBSFA) boundaries, the restricted Makuapu‘uhonua, the vessel transit boundary, and the ‘opihi (limpet, Cellana spp.) management area with coordinates [28] and (b)survey stations from all five years within and outside the CBSFA boundaries.

2.2. Fish and Benthic Surveys

Fish counts and sizes were estimated inside and outside the CBSFA using the Kaua‘iAssessments of Habitat Utilization (KAHU) rapid assessment technique over a five yearperiod [28]. This method employed 25 × 5 m belt transects [29], designed by the UH FERLFish Habitat Utilization Study (FHUS). The time frame of this study included surveysconducted in August 2016 (n = 55 inside, n = 43 outside), August 2017 (n = 59 inside, n = 49outside), August 2018 (n = 71 inside, n = 32 outside), and June and August in 2019 (n = 58inside, n = 40 outside), and June and August in 2020 (n = 79 inside, n = 44 outside). At eachstation, a 25 m transect was deployed in the direction of a pre-determined compass bearing(0◦, 90◦, 180◦, or 270◦). All fish species, counts, and sizes (total length [cm]) were recordedwithin a 5-m swath (125 m2 total area per transect) for a minimum of 10 min [28]. Fishsurveyors annually participated in calibration dives to account for observer variability.

A benthic surveyor followed the fish diver to quantify habitat types associated witheach transect [28]. Photographs were taken of the substrate on a previously calibratedCannon S100 camera at every 1 m mark at a 90◦ angle to the transect (n = 26). Invertebrate(urchins and sea cucumbers) abundances were also recorded. The surveyor also enumer-ated macroinvertebrates within a 2 m × 25 m swath (50 m2) along the transect line. Thetwenty-six benthic photographs collected at each transect were later processed by overlay-ing 30 points per photograph using the benthic image analysis program CoralNet [28].

2.3. Assessing Reproductive Maturity

L50 values—defined as the size at which half of the individuals in a population havereached reproductive maturity—were obtained from previous studies within the mainHawaiian Islands (MHI) (Table A1). Female L50 values were used rather than male L50values owing to the disproportionate importance of large females in population repro-ductive output [17,19,29–31]. Careful consideration was taken to use L50 values that weremost appropriate for Ha‘ena because L50 values have been shown to vary within andamong islands in Hawai‘i between individuals within a species [26]. Acanthurus triostegus(convict tang, manini), which is an endemic subspecies, was the only resource fish with L50values derived from gonad measurement on the northshore of Kaua‘i. The majority of theremaining L50 values were selected from similar reproductive studies conducted within

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the Hawaiian Archipelago [15–20,23,25,26,32]. A select few L50 values were chosen fromreproductive studies from the Northwestern Hawaiian Islands (NWHI) [32] or Papua NewGuinea (Kyphosus spp., lowfin chub, nenue) [33]. Resource fish species with L50 values fromoutside the Hawaiian Archipelago, with the exception of the one Papua New Guinea study,were excluded from our analyses.

2.4. Statistical Analysis

Changes in assemblage composition of resource fishes throughout the five years insideand outside the CBSFA were analyzed using both multivariate and univariate statistics inthe R statistical software. Simpson’s diversity, Menhinick’s richness, and Pielou’s evennesswere also calculated using biomass metrics for the resource fish assemblage. Biomass wascalculated using the following equation:

W = a × (standard length)b, (1)

where standard length in cm was converted from total length and the a and b parameterswere obtained from the Hawai‘i Cooperative Fishery Research Unit database. Non-metricmultidimentional scaling (nMDS) is a rank-based analysis that was used to visualizetrophic level patterns in resource fish assemblages. Fish taxa were categorized into trophiccategories (corallivores, herbivores, mobile invertebrate feeders, sessile invertebrate feeders,piscivores, zooplanktivores, and detritivores) according to various published sources andFishBase (www.fishbase.org (accessed on 7 March 2021)). Species that occurred in <5% ofstations were eliminated prior to analysis. Biomass was down-weighted to account for rarespecies and a Bray–Curtis dissimilarity matric was used. Permutation-based multivariateanalysis of variance (PERMANOVA) was conducted using Type II sum of squares ontrophic level (herbivore, piscivore, planktivore, and mobile invertivore) matrices to assesspatterns of resource fish species assemblages through time (2016, 2017, 2018, 2019, 2020)and location (inside or outside). Homogeneity of variances were assessed for reliability ofthe PERMANOVA results using the PERMDISP2 procedure [34]. Pairwise comparisonswith a Holm’s correction was applied following significant (p < 0.05) PERMANOVA results.SIMPER (similarity percentages) were used to analyze resource species with the highestinfluence on the multivariate test; however, unequal sample sizes may result in unreliableSIMPER results [35]. Therefore, further univariate analyses were conducted to determineresource species patterns.

Univariate analyses included ANOVA with Holm’s correction on multiple pairwisecomparisons on quarter-root or log10 transformed data. However, the large number ofzeros in most of the datasets meant zero-inflated models were most appropriate. Due tothe inability to run biomass, a non-integer response variable, through zero-inflated models,data were manually split into two sections and generalized linear models (GLMs) wereused. Non-normal data were first divided into presence/absence to account for the highnumber of zeros present. A binomial distribution with a complementary-log–log linkfunction was used for presence/absence data to account for the unequal number of zeros-to-ones in each matrix. Non-zero biomass data were analyzed using a Gamma distributionwith a log-link function run on quarter-root transformed biomass. Least-squares meanswere applied to GLM models to examine pairwise comparisons.

Finally, distance-based RDAs (redundancy analyses) (dbRDAs) were used to comparebiomass matrices with depth, habitat type, and percent cover of coral, calcareous corallinealgae (CCA), turf, and macroalgae and abundances of invertebrates, to examine drivers ofpatterns in fish biomass over time and between management regimes. Turf was excludedfrom the analysis, as it was found to be highly correlated with CCA. An Akaike informationcriterion (AIC) forward and backwards step model selection was used to determine thecritical variables to run in the dbRDA model using a Bray–Curtis index.

All analyses were run in the R statistical software using the following packages:tidyverse, pscl, MASS, rstatix, vegan, multcomp, multcompView, lsmeans, corrplot, GGally,ggplot2, gridExtra, ggpubr, plyr, and dplyr [36].

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3. Results

A total of 156 fish species were surveyed over the 5 year period across all locations.Mobile invertivores and herbivores occurred at over 90% of the total stations, while plank-tivores, corallivores, and piscivores occurred at >50% of the stations surveyed. Of the156 species, 32 (20.5%) were classified as resource fishes by the Ha‘ena community, andwere composed mainly of herbivores (82.8%) and piscivores (48.5%). After eliminatingspecies that occurred in <5% of the stations, 65 fish species overall and 19 resource fishspecies remained for analyses.

3.1. Overall Fish Assemblage

Fish assemblage structure showed a high degree of overlap among years (Figure 2a).Assemblage structure outside the CBSFA was more concordant and was a subset of theassemblage inside the CBSFA (Figure 2b). Clear spatial patterns in overall fish asseblageswere evident among sub-locations in the nMDS plot (Figure 2c). Fish assemblages in-side and outside shallow (<7 m) and inside and outside deep (≥7 m) strata had similarassemblage structures, while the Makua pu‘uhonua had a distinct assemblage (Figure 2c).

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(a) (b)

(c)

Figure 2. Non-metric multidimentional scaling (nMDS) plots of overall fish species assemblages by (a) 5 years, (b) inside or outside, and (c) sub-location divisions with depth incorporated. Clear spatial patterns in overall fish asseblages are evident in the sub-location nMDS. See Table A1 for species code identification. Sub-location codes are as follows: Hāʻena Inside Deep (HID), Hāʻena Inside Shallow (HIS), Hāʻena Outside Deep (HOD), Hāʻena Outside Shallow (HOS), and Puʻuhonua (PU; located within Hāʻena Inside Shallow).

3.2. Resource Fish Assemblages (Above L50) The diversity of reproductively mature resource fishes (i.e., individuals above their

respective L50 values) significantly increased from 2016, 2017, and 2018 to 2020 (𝜒2 = 3.46, p = 0.01), likely in part to increased species richness from 2017 and 2018 to 2020 (F4,447 = 2.84, p = 0.02). Evenness did not differ significantly over the 5-year period (p = 0.06) or between inside and outside the CBSFA boundaries (p = 0.31). Diversity was significantly higher inside the CBSFA compared to outside (𝜒2 = 5.33, p = 0.02), while species richness remained similar between locations (p = 0.21).

3.2.1. Trophic Level Assemblage Reproductively mature trophic level resource fishes had distinct assemblages in 2016

and 2017, while no distinctions were evident between 2017, 2018, 2019, and 2020. There were small differences between the fish assemblages at deep water stations inside the CBSFA compared to deep water stations outside the boundaries. Shallow water stations inside and outside the boundaries have similar assemblages, yet were distinct from deeper

Figure 2. Non-metric multidimentional scaling (nMDS) plots of overall fish species assemblages by (a) 5 years, (b) inside oroutside, and (c) sub-location divisions with depth incorporated. Clear spatial patterns in overall fish asseblages are evidentin the sub-location nMDS. See Table A1 for species code identification. Sub-location codes are as follows: Ha‘ena InsideDeep (HID), Ha‘ena Inside Shallow (HIS), Ha‘ena Outside Deep (HOD), Ha‘ena Outside Shallow (HOS), and Pu‘uhonua(PU; located within Ha‘ena Inside Shallow).

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Simpson’s diversity did not significantly differ among the 5 years (F4,518 = 0.95,p = 0.44). Species richness was significantly lower in 2019 compared to 2016 (F4,518 = 2.51,p = 0.04), while evenness increased from 2017 to 2018, and declined again in 2020 (χ2 = 2.82,p = 0.02). While diversity and richness did not differ between locations (F1,518 = 0.57,p = 0.45 and F1,518 = 0.61, p = 0.43, respectively), evenness was significantly higher outsidecompared to inside the CBSFA (χ2 = 12.8, p < 0.001).

3.2. Resource Fish Assemblages (above L50)

The diversity of reproductively mature resource fishes (i.e., individuals above theirrespective L50 values) significantly increased from 2016, 2017, and 2018 to 2020 (χ2 = 3.46,p = 0.01), likely in part to increased species richness from 2017 and 2018 to 2020 (F4,447 = 2.84,p = 0.02). Evenness did not differ significantly over the 5-year period (p = 0.06) or betweeninside and outside the CBSFA boundaries (p = 0.31). Diversity was significantly higherinside the CBSFA compared to outside (χ2 = 5.33, p = 0.02), while species richness remainedsimilar between locations (p = 0.21).

3.2.1. Trophic Level Assemblage

Reproductively mature trophic level resource fishes had distinct assemblages in 2016and 2017, while no distinctions were evident between 2017, 2018, 2019, and 2020. Therewere small differences between the fish assemblages at deep water stations inside theCBSFA compared to deep water stations outside the boundaries. Shallow water stationsinside and outside the boundaries have similar assemblages, yet were distinct from deeperwater stations. Trophic level resource fish biomass revealed significant differences amongyears (PERMANOVA, Table 1). Specifically, pairwise comparisons detected higher biomassin 2017 and 2019 compared to 2016 (Pairwise, Table 2).

Table 1. PERMANOVA results for trophic level matrix across year and location. Bold numbersindicate significance. Df = degrees of freedom, SS = sum of squares, MS = mean squares. Significantvalues in bold.

Df SS MS Pseudo-F R2 p

Location 1 0.58 0.58 2.114 0.004 0.052Year 4 2.52 0.63 2.292 0.019 0.001

Location:Year 4 0.97 0.24 0.879 0.007 0.613Residuals 433 119.11 0.28 0.911

Table 2. Pairwise comparison with holm’s adjustment results for trophic level matrix across years.Significant values in bold.

Pairs F.Model R2 p-Value p-Adjusted

2016 vs.2017 3.520 0.021 0.001 0.0102016 vs. 2018 2.151 0.012 0.046 0.2762016 vs.2019 5.565 0.034 0.001 0.0102016 vs. 2020 2.932 0.015 0.014 0.1122017 vs. 2018 1.929 0.011 0.071 0.3552017 vs. 2019 0.859 0.005 0.500 0.7522017 vs. 2020 1.711 0.008 0.115 0.4602018 vs. 2019 2.503 0.014 0.020 0.1402018 vs. 2020 1.045 0.005 0.376 0.7522019 vs. 2020 1.422 0.007 0.199 0.597

Reproductively mature herbivores and piscivores had higher abundances in 2020compared to 2016 (χ2 = 7.15, p = 0.041 and χ2 = 7.68, p = 0.025, respectively). There was ahigher number of herbivores inside the CBSFA (χ2 = 18.05, p < 0.001), while planktivoreswere more abundant outside the boundary (χ2 = 4.09, p = 0.045). Of the species present,mobile invertivores and planktivores had a higher biomass outside (χ2 = 11.83, p < 0.001

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and χ2 = 4.41, p = 0.039, respectively), while herbivore and piscivore biomass was notsignificantly different by year or location (both p > 0.05).

Despite similar assemblage compositions through time and locations, specific specieslevel changes were analyzed to examine species-level differences. Ten herbivore species,six piscivore species, and four mobile invertivore species were further analyzed. Spatialordination plots did not reveal any distinct species assemblages through time or locationfor piscivores or mobile invertivores; however, herbivore species may be responsible forthe distinction in assemblages between 2016 and 2017. Herbivore biomass in 2016 wassignificantly lower compared to 2020 in pairwise comparisons (pairwise, F4,438 = 1.77,p = 0.002).

Specific herbivorous species patterns show that A. triostegus, A. blochii (ringtail sur-geonfish, pualu) and Naso lituratus (orangespine unicornfish, umaumalei) presences werehigher inside the CBSFA (χ2 = 9.9, p = 0.002; χ2 = 6.6, p = 0.006; χ2 = 6.0, p = 0.013, respec-tively; Figure 3). N. lituratus was the only species that showed a significant trend, with adecrease in presence over the 5 year period (χ2 = 8.72, p = 0.022; Figure 3).

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Figure 3. Generalized linear model (GLM) plots of significant trends in both presence/absence and biomass data among locations (ACTR, ACBL, and NALI-presence/absence), years (KYSP, NALI-biomass), and interaction between years and locations (ACTR). Species codes are as follows: A. triostegus (ACTR), A. blochii (ACBL), N. lituratus (NALI), and Kyphosus spp. (KYSP).

Reproductively mature piscivores showed a significant difference among years outside the CBSFA (PERMANOVA, F4,85 = 1.80, p = 0.037), where 2018 biomass was significantly higher compared to 2016 (p = 0.01). This was likely caused by the combination of Aprion virescens (green jobfish, uku) having higher presence outside the boundaries (𝜒2 = 8.79, p = 0.006; Figure 4) and Caranx melampygus (blue trevally, omilu) biomass increasing both inside and outside the CBSFA boundaries in later years (Year: 𝜒2 = 7.11, p = 0.023; Location: 𝜒2 = 5.33, p = 0.026; Interaction: 𝜒2 = 5.15, p = 0.028; Figure 4).

Figure 3. Generalized linear model (GLM) plots of significant trends in both presence/absence and biomass data amonglocations (ACTR, ACBL, and NALI-presence/absence), years (KYSP, NALI-biomass), and interaction between yearsand locations (ACTR). Species codes are as follows: A. triostegus (ACTR), A. blochii (ACBL), N. lituratus (NALI), andKyphosus spp. (KYSP).

Reproductively mature piscivores showed a significant difference among years outsidethe CBSFA (PERMANOVA, F4,85 = 1.80, p = 0.037), where 2018 biomass was significantlyhigher compared to 2016 (p = 0.01). This was likely caused by the combination of Aprionvirescens (green jobfish, uku) having higher presence outside the boundaries (χ2 = 8.79,p = 0.006; Figure 4) and Caranx melampygus (blue trevally, ‘omilu) biomass increasing bothinside and outside the CBSFA boundaries in later years (Year: χ2 = 7.11, p = 0.023; Location:χ2 = 5.33, p = 0.026; Interaction: χ2 = 5.15, p = 0.028; Figure 4).

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Figure 4. Generalized linear model (GLM) plots of significant trends in both presence/absence and biomass between loca-tions (APVI-presence/absence and CAME-biomass), and among years (CAME-biomass). Species codes are as follows: A. virescens (APVI) and C. melanpygus (CAME).

Although spatial patterns were not evident in the mobile invertivore nMDS plots, the PERMANOVA identified a significant location term where reproductively mature mobile invertivore biomass was higher outside the boundaries (p = 0.009). This was likely driven by the higher presence of Mulloidichthys flavolineatus (yellowstripe goatfish, weke) inside the boundaries, as well as higher biomass of the introduced Lutjanus kasmira (bluestripe snapper, taʻape) outside the boundaries (𝜒2 = 6.47, p = 0.018 and 𝜒2 = 9.41, p = 0.003, respectively; Figure 5).

Figure 5. GLM plots of significant trends in both presence/absence and biomass data among locations (MUFL-presence/ab-sence and LUKA-biomass). Species codes are as follows: Mulloidichthys flavolineatus (MUFL) and Lutjanus kasmira (LUKA).

3.2.2. Fish and Benthic Community Relationships Depth and invertebrate abundance only explained ~5% (adj R2) of the variance within

reproductively mature resource fishes at the trophic assemblage level. Variations in the biomass of planktivores, piscivores, and mobile invertivores (5% adj R2; Figure 6a,d) were explained by depth, while the variance in herbivore biomass was mainly explained by the abundance of invertebrates (6% adj R2, Figure 6a,c). Within the piscivore assemblage, CCA, macroalgae (Mac), and depth explained ~ 6% (adj R2) of the variability in C.

Figure 4. Generalized linear model (GLM) plots of significant trends in both presence/absence and biomass betweenlocations (APVI-presence/absence and CAME-biomass), and among years (CAME-biomass). Species codes are as follows:A. virescens (APVI) and C. melanpygus (CAME).

Although spatial patterns were not evident in the mobile invertivore nMDS plots, thePERMANOVA identified a significant location term where reproductively mature mobileinvertivore biomass was higher outside the boundaries (p = 0.009). This was likely drivenby the higher presence of Mulloidichthys flavolineatus (yellowstripe goatfish, weke) insidethe boundaries, as well as higher biomass of the introduced Lutjanus kasmira (bluestripesnapper, ta‘ape) outside the boundaries (χ2 = 6.47, p = 0.018 and χ2 = 9.41, p = 0.003,respectively; Figure 5).

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Figure 4. Generalized linear model (GLM) plots of significant trends in both presence/absence and biomass between loca-tions (APVI-presence/absence and CAME-biomass), and among years (CAME-biomass). Species codes are as follows: A. virescens (APVI) and C. melanpygus (CAME).

Although spatial patterns were not evident in the mobile invertivore nMDS plots, the PERMANOVA identified a significant location term where reproductively mature mobile invertivore biomass was higher outside the boundaries (p = 0.009). This was likely driven by the higher presence of Mulloidichthys flavolineatus (yellowstripe goatfish, weke) inside the boundaries, as well as higher biomass of the introduced Lutjanus kasmira (bluestripe snapper, taʻape) outside the boundaries (𝜒2 = 6.47, p = 0.018 and 𝜒2 = 9.41, p = 0.003, respectively; Figure 5).

Figure 5. GLM plots of significant trends in both presence/absence and biomass data among locations (MUFL-presence/ab-sence and LUKA-biomass). Species codes are as follows: Mulloidichthys flavolineatus (MUFL) and Lutjanus kasmira (LUKA).

3.2.2. Fish and Benthic Community Relationships Depth and invertebrate abundance only explained ~5% (adj R2) of the variance within

reproductively mature resource fishes at the trophic assemblage level. Variations in the biomass of planktivores, piscivores, and mobile invertivores (5% adj R2; Figure 6a,d) were explained by depth, while the variance in herbivore biomass was mainly explained by the abundance of invertebrates (6% adj R2, Figure 6a,c). Within the piscivore assemblage, CCA, macroalgae (Mac), and depth explained ~ 6% (adj R2) of the variability in C.

Figure 5. GLM plots of significant trends in both presence/absence and biomass data among locations (MUFL-presence/absence and LUKA-biomass). Species codes are as follows: Mulloidichthys flavolineatus (MUFL) and Lutjanuskasmira (LUKA).

3.2.2. Fish and Benthic Community Relationships

Depth and invertebrate abundance only explained ~5% (adj R2) of the variance withinreproductively mature resource fishes at the trophic assemblage level. Variations in thebiomass of planktivores, piscivores, and mobile invertivores (5% adj R2; Figure 6a,d) wereexplained by depth, while the variance in herbivore biomass was mainly explained by theabundance of invertebrates (6% adj R2, Figure 6a,c). Within the piscivore assemblage, CCA,

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macroalgae (Mac), and depth explained ~ 6% (adj R2) of the variability in C. melampygusand A. virescens (Figure 6b). The majority of variance in each of the resource fish matricesremains unexplained by the variables included.

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melampygus and A. virescens (Figure 6b). The majority of variance in each of the resource fish matrices remains unexplained by the variables included.

(a) (b)

(c) (d)

Figure 6. Distanced-based redundancy analyses (dbRDA) plots of (a) trophic level correlations, (b) piscivore correlations, (c) herbivore correlations, and (d) mobile invertivore correlations with benthic substrate components.

4. Discussion Examining assemblage composition shifts and distributions through time is im-

portant in assessing the effectiveness of protected areas to implement adaptive manage-ment strategies [37,38]. Our overall results found no major shifts in fish assemblage com-position in space or time inside or outside the Hāʻena CBSFA. Furthermore, the dbRDA models comparing resource fish biomass to depth and benthic community composition estimates explained very little of the variance in resource fish above their L50 values (Fig-ure 6). While overall, reproductively mature resource fish assemblages remained fairly constant over time, species level shifts were evident. The significantly increasing biomass values of the piscivorous C. melanpygus outside the CBSFA boundaries in later years may suggest early signs of spillover. Positive increases in reproductively mature resource fish diversity and richness through time and between management regimes, as well as posi-tive trends of the species that show significant relationships through time and location suggest continual monitoring can be beneficial.

Figure 6. Distanced-based redundancy analyses (dbRDA) plots of (a) trophic level correlations, (b) piscivore correlations,(c) herbivore correlations, and (d) mobile invertivore correlations with benthic substrate components.

4. Discussion

Examining assemblage composition shifts and distributions through time is importantin assessing the effectiveness of protected areas to implement adaptive managementstrategies [37,38]. Our overall results found no major shifts in fish assemblage compositionin space or time inside or outside the Ha‘ena CBSFA. Furthermore, the dbRDA modelscomparing resource fish biomass to depth and benthic community composition estimatesexplained very little of the variance in resource fish above their L50 values (Figure 6). Whileoverall, reproductively mature resource fish assemblages remained fairly constant overtime, species level shifts were evident. The significantly increasing biomass values of thepiscivorous C. melanpygus outside the CBSFA boundaries in later years may suggest earlysigns of spillover. Positive increases in reproductively mature resource fish diversity andrichness through time and between management regimes, as well as positive trends of the

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species that show significant relationships through time and location suggest continualmonitoring can be beneficial.

While few species level trends may be evident, the lack of major shifts in overallbiomass at a temporal or spatial scale in reproductively mature resource fish assemblagesmay indicate several possible outcomes: (1) the CBSFA is having no effect, (2) five yearsis not sufficient time to see an effect, (3) habitats within and outside the CBSFA may bedissimilar, (4) the CBSFA boundaries are too small to show an effect, and/or (5) low samplesizes resulted in low statistical power. It is likely that the five year period may not havebeen sufficient time to begin seeing reserve effects. The Kahekili Herbivore ManagementArea on Maui took > 6 years before effects were witnessed [7]. It is also possible that the2018 record breaking freshwater flood event may have delayed the effects of protection.This freshwater event caused major landslides and flooding that resulted in significantlylower total fish biomass on shallow Ha‘ena reefs [39]. Furthermore, the nMDS plotsof the overall assemblage, trophic level, herbivore, and piscivore matrices demonstrateoverlap on a spatial scale between inside and outside assemblages at the shallow stationsand overlapping inside and outside assemblages at the deep stations. These patterns areconsistent with the study design in comparing similar habitats within and outside theboundaries and are consistent with a lack of influx of resource fishes moving into theCBSFA if individuals are acquiring equal resources both inside and outside the boundaries.

The size and mosaic of habitats of an MPA can greatly affect the success of that areaand the Ha‘ena CBSFA may be too small for the mobilities of resource fishes that weexamined [40]. Within such a diverse group of organisms with varying functional rolesand demands for energy [41], it is necessary for some species of fishes to travel longerdistances to fulfill their energy, reproductive, and social requirements [42–44]. Althoughthe majority of the Ha‘ena CBSFA is not fully protected against fishing, it is one of thelarger MPAs in the state at roughly 8 km2, with is larger than the median size (1.2 km2) ofMPAs in Hawai‘i [2]. Yet, considering the effective size for MPAs (10 to 100 km2 [2], theHa‘ena CBSFA remains below the lower threshold.

Although sample sizes were >30 for all stations by year and location, limitations insample sizes of individuals were evident for select species. Therefore, uncommon specieswith low sample sizes were excluded by eliminating species that occurred in <5% ofstations for the purpose of improved detection of an MPA effect. Even in unfished regions,fish population distributions are naturally skewed to smaller individuals that are moreabundant in size and as they become larger, their abundance decreases.

L50 values can vary not only by location and water temperature but also by seasonand year [45,46]. Although the L50 value for Acanthurus triostegus came directly from gonadmeasurements of individuals located around the Ha‘ena area, L50 values of most otherspecies were derived from measurements of individuals from the MHI, some from theNWHI, and one from Papua New Guinea (Table A1). L50 values are the best estimate thatcan be used to assess reproductive maturity using in situ observations. Hence, conductingfurther research to acquire L50 values for all resource fish species from the Ha‘ena regioncould be beneficial in ensuring precise analyses of that specific location.

5. Conclusions

Overall resource fish assemblage composition did not change temporally or spatiallyover five years despite the implementation of fishing regulations. Results of this studysuggest continuing annual surveys to evaluate long-term trends in order to better predicthow resource fish assemblages may be changing within the current management regime.These monitoring data are essential if future adaptive changes in rules and regulationsare to be implemented and effective. Furthermore, determining the habitat types andbenthic structures that are benefical to specific resource fishes at the Ha‘ena CBSFA iscrucial in assessing any emerging patterns of assemblage composition. Such contemporaryresearch and management practices serve the purpose of providing data that allowslocal stakeholders and communities to adjust their rules and regulations as needed, thus,

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allowing an adaptive management strategy to be implemented [47]. CBSFAs place localand traditional knowledge and practices at the forefront of fisheries management, allowingaccountability for fisheries by local community members [37]. One of the social side effectsof most MPAs in Hawai‘i is the prevention of local fishers from practicing traditionalfishing methods or incorporating adaptive management strategies due to temporary orpermanent closures [48]. The integration of local and traditional knowledge and practicesinto the CBSFA design gives local people who use the protected area on a frequent basisaccountability and a voice that can be beneficial in creating rules and regulations, especiallyas there are many communities that still depend on marine resources for subsistence.

Author Contributions: Conceptualization, K.S.R.; methodology, K.S.R.; validation, R.M.W.; formalanalysis, R.M.W. and A.M.F.; investigation, R.M.W.; data curation, R.M.W.; writing—original draftpreparation, R.M.W.; writing—review and editing, R.M.W., K.S.R., A.M.F. and C.L.H.; visualization,R.M.W.; supervision, K.S.R. and C.L.H.; funding acquisition, K.S.R. All authors have read and agreedto the published version of the manuscript.

Funding: This research was funded by the State of Hawai‘i Department of Land and NaturalResources, Division of Aquatic Resources, award grant number 35228.

Institutional Review Board Statement: Ethical review and approval were waived for this study, dueto methodologies that do not disturb or alter the marine biota.

Informed Consent Statement: Not applicable.

Data Availability Statement: Data publicly available at the Division of Aquatic Resources andthe University of Hawai‘i, Hawai‘i Institute of Marine Biology Coral Reef Ecology Laboratory(crel@hawaii.edu).

Acknowledgments: We acknowledge the assistance and dedication of the Hui Makaainana o Makanacommunity group for their input and support of this project. We are extremely grateful to the Divisionof Aquatic Resources (DAR) for their extensive effort and resources provided in planning, diving,and project guidance. Special thanks to DAR Acting Director: Brian Nielsen and the DAR Kaua‘iMonitoring Team: Heather Ylitalo-Ward, Ka‘ilikea Shayler, McKenna Allen, and Mia Melamed, DARO‘ahu Monitoring and Algal Invasive Species teams: Paul Murakawa, Kazuki Kageyama, HarukoKoike, Tiffany Cunanan, Justin Goggins, and Kimberly Fuller, DAR Maui Monitoring Team: RussellSparks, Kristy Stone, Linda Castro, Tatiana Martinez, and DAR biostatistician Haruko Koike. Weappreciate the efforts of the Coral Reef Ecology Lab: Anita Tsang, Sarah Severino, Justin Han, AndrewGraham, Yuko Stender, Angela Richards Dona, Keisha Bahr, Ashley McGowan for surveys and dataanalyses and Kostas Stamoulis and Jade Delevaux for additional fish behavior surveys. DAR Kaua‘iEducation and Outreach Specialist: Katie Nalesere. These surveys would not have been possiblewithout the vessels and operators provided by the DLNR: Division of Boating and Ocean Recreation(DOBOR), Kaua‘i under the direction of Joseph Borden. This is publication number 101 from theSchool of Life Sciences, University of Hawai‘i at Manoa.

Conflicts of Interest: The authors declare no conflict of interest. The funders had a role in the designof the study, and in the collection of the data.

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Appendix A

Table A1. List of resource fish species with sources from which L50 values were derived. Species derived from the Ha‘ena resource fish species list are noted with an asterisk (*) [13–27,32,33].

Family Code Taxon Name Common Hawaiian Endemism L50 (cm) Citation

Acanthuridae ACBL Acanthurus blochii Ringtail Surgeonfish pualu Native 27.6 Choat and Robertson, 2002, Kritzer, 2001ACDU Acanthurus dussumieri * Eye-stripe Surgeonfish palani Native 28.2 Choat and Robertson, 2002, Kritzer, 2001ACNR Acanthurus nigroris * Bluelined Surgeonfish maiko Native 15.7 DiBattista et al., 2010ACTR Acanthurus triostegus * Convict Tang manini Endemic 13.2 Schemmel and Friedlander, 2017NABR Naso brevirostris Spotted Unicornfish kala lolo Native 26.9 Choat and Robertson, 2002, Kritzer, 2001NAHE Naso hexacanthus Sleek Unicornfish kala holo Native 51.1 Choat and Robertson, 2002, Kritzer, 2001NALI Naso lituratus Orangespine Unicornfish umaumalei Native 25 Kritzer, 2001

NAUN Naso unicornis * Bluespine Unicornfish kala Native 33 Nadon et al., 2015, Eble et al., 2009

Carangidae CAME Caranx melampygus * Blue Trevally ‘omilu Native 47.5 Sudekum et al., 1991, Nadon et al., 2015CAOR Carangoides orthogrammus * Island Jack ulua Native 45.4 Nadon and Ault, 2016SECR Selar crumenophthalmus * Big-Eyed Scad akule Native 17 FishBaseSEDU Seriola dumerili * Amberjack kahala Native 99.5 FishBase

Holocentridae MYBE Myripristis berndti Bigscale Soldierfish ‘u‘u Native 17.5 Murty, 2002, Craig and Franklin, 2008,Kritzer, 2001

Kyphosidae KYSP Kyphosus species * Lowfin Chub nenue Native 25.3 Longnecker et al., 2013Lethrinidae MOGR Monotaxis grandoculis Bigeye Emperor mu Native 38.9 Nadon and Ault, 2016Lutjanidae APVI Aprion virescens Green Jobfish uku Native 50 Everson, 1989, Nadon, 2017

LUFU Lutjanus fulvus Blacktail Snapper to‘au Introduced 24 Nadon and Ault, 2016LUKA Lutjanus kasmira Bluestripe Snapper ta‘ape Introduced 20 Allen, 1985, Kritzer, 2001

Mullidae MUFL Mulloidichthys flavolineatus * Yellowstripe Goatfish weke Native 19.9 Cole, 2009, Nadon, 2017MUVA Mulloidichthys vanicolensis * Yellowfin Goatfish weke ‘ula Native 20.6 Cole, 2009, Kritzer, 2001PACY Parupeneus cyclostomus Blue Goatfish moano kea Native 26.9 Nadon and Ault, 2016PAPO Parupeneus porphyreus * Whitesaddle Goatfish kumu Endemic 26.4 Nadon et al., 2015

Scaridae CACA Calotomus carolinus * Stareye Parrotfish Native 24.3 DeMartini and Howard, 2016CHPE Chlorurus perspicillatus * Spectacled Parrotfish uhu uliuli Endemic 34.5 DeMartini and Howard, 2016CHSO Chlorurus spilurus Native 17.2 DeMartini and Howard, 2016SCDU Scarus dubius * Regal Parrotfish lauia Endemic 23.2 Nadon and Ault, 2016SCPS Scarus psittacus * Palenose Parrotfish uhu Native 13.9 DeMartini and Howard, 2016SCRU Scarus rubroviolaceus * Redlip Parrotfish palukaluka Native 35 DeMartini and Howard, 2016

Epinephelidae CEAR Cephalopholis argus Blue-spotted Grouper Introduced 20 Schemmel et al., 2016

* = Ha‘ena species list.

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